一种车用单刀双掷开关的质检仪器研发

介绍了一种车用单刀双掷开关质量检测仪器的研发,包括检测系统的硬件设计、检测原理以及算法软件开发。首先,根据单刀双掷开关的工作原理及质量检测要求,开发了检测手指触觉压力的机械装置,以及控制开关通道切换的继电器控制电路;在此基础上,开发了相应的检测算法,可实现对开关切换状态、电压差以及手指触觉压力进行自动化的数据采集、分析、检测和判断,从而挑选出合格的开关;最后,设计了触屏人机交互界面,可实时显示产品检测的参数。该检测设备在某自动化生产线上经过近一年时间的检测验证,证明系统运行平稳,检测效果良好、误检率低,可以完全取代传统的人工检测方式,提高了开关的检测效率,降低了检测人员的工作量。     

Efficiency assessment of primary care providers: A conditional nonparametric approach

This paper uses a fully nonparametric approach to estimate efficiency measures for primary care units incorporating the effect of (exogenous) environmental factors. This methodology allows us to account for different types of variables (continuous and discrete) describing the main characteristics of patients served by those providers. In addition, we use an extension of this nonparametric approach to deal with the presence of undesirable outputs in data, represented by the rates of hospitalization for ambulatory care sensitive condition (ACSC). The empirical results show that all the exogenous variables considered have a significant and negative effect on efficiency estimates.     

An analysis of managerialism and performance in English and Welsh male prisons

This paper fills a noticeable gap in the current economic and penology literature by proposing new performance-enhancing policies based on an efficiency analysis of a sample of male prisons in England and Wales over the period 2009/10. In addition, we advance the empirical literature by integrating the managerialism of four strategic functions of prisons, employment and accommodation, capacity utilization, quality of life in prison and the rehabilitation and re-offending of prisoners. We find that by estimating multiple models focussing on these different areas some prisons are more efficient than other establishments. In terms of policy, it is therefore necessary to consider not just an overall performance metric for individual prisons, as currently undertaken annually by the UK Ministry of Justice, but to look into the administration and managerialism of their main functions in both a business and public policy perspective. Indeed, it is further necessary to view prisons together and not as single entities, so as to obtain a best practice frontier for the different operations that management undertakes in English and Welsh prisons.     

基于LabVIEW的超导磁体数采与测控系统研究

根据研制的大口径、高场强的超导强磁装置的特点,选择基于LabVIEW的数据采集和测控系统。低温传感器共六支,分别安装于磁体氦槽和冷屏不同位置,其中碳电阻温度计Cernox三支,铂电阻温度计Pt100两支、Pt1000一只,用于监测磁体温度;超导氦液面计一个,用于监测氦液面高度;根据磁体参数匹配研制了一套可调励磁速率的磁体专用直流电源及其失超保护系统,保证磁体安全运行。所有数据通过NI公司内插DAQ6251数据采集卡的PX I采集,用LabVIEW编写数据采集程序和人机界面。     

应用单波段亮度变化率获取无云干扰的卫星数据

在卫星遥感检测中,云对数据的反演带来了严重的干扰。精确地确定云区及减弱云的干扰是具有挑战性的问题。根据云的大气辐射特性给出了卫星图像亮度变化率计算公式、图像反射率的变化率计算公式和热红外波段图像亮度温度变化率的计算方法。利用单波段亮度变化率检测卫星数据中云干扰相对强度的新方法,快速、精确地获取云区位置,得到云区以外无云干扰的数据。根据各光谱波段具有不同的透射云层能力的特点,文章还给出了卫星各红外波段对云层透射能力的客观评价以及影响透射效果的因素。在此基础上利用红外波段对伪卷云透射图像数据完成了多波段数据融合。很好地恢复了可见光波段云区下面的真实数据。统计数据表明,融合后的云区图像数据与可见光波段相关性保持一致。     

Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion

Leccinum rugosiceps is an edible mushroom belonging to genus Leccinum of Boletaceae. Its fruiting bodies are richer in nutrients than many vegetables and fruit. The model of support vector machine was established for the discrimination of L. rugosiceps from regions based on rapid and low-cost ultraviolet and infrared spectroscopies. The mid-level data fusion was performed by support vector machine. Compared to a single spectroscopic technique, mid-level data fusion provided higher accuracy by selecting the most significant variance from data matrixes based on partial least squares discriminant analysis. The accuracy of the classification of samples in the calibration and test sets were 85.00 and 94.74%, higher than separate measurements by ultraviolet or infrared spectroscopy. This approach has applications for authentication and quality assessment of L. rugosiceps.     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts

Models for weather and climate prediction are complex, and each model typi-cally has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation (MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model''s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs for short) as an imperfect tropical climate model and stochastic differential equations (SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.     

渤黄东海底摩擦系数数值研究

使用伴随同化方法探讨了四种底摩擦系数处理方法并模拟了渤黄东海的M₂分潮.理想实验表明:伴随同化方法有很强的反演底摩擦系数的能力;为了得到较好的反演结果,反演策略必须与给定的分布一致;如果给定分布很复杂,则需要复杂的反演策略.实际实验表明,底摩擦效应与地形密切相关,而海洋地形十分复杂,故需要较多的独立的底摩擦系数才能得到较好的模拟结果.本文的第四种方法将每一点均作为独立的底摩擦系数,在实验中获得了成功,验证了这种方法的合理性和有效性.